Complex problems.
Thoughtful systems.
Real-world impact.

The problem

Legal professionals needed cited natural-language analysis of agreements in a dependable application, rather than a prototype tied to an individual development environment.

My contribution

Owned the engineering pieces: model deployment, backend services, frontend development, and AWS infrastructure. Moved the application to a managed, internally accessible service; built its native React interface, access controls, and operational monitoring in collaboration with the project team.

The outcome

• Actively used by 50+ legal professionals at Amazon.
• Cited natural-language contract analysis delivered through a native application.
• Managed deployment, access controls, and monitoring supporting ongoing use.

From a prototype tunnel to a managed application

Took the service from an individual development tunnel to a managed AWS deployment reachable on the corporate network. Owned infrastructure provisioning, model deployment integration, load-balanced service hosting, alarms, and operational fixes.

Replace embedded UI with a native experience

Built a native React frontend and a compatibility API for existing backend functionality, with access gates and request-recording support. This connected the application to its host platform without presenting a separate embedded interface.

Engineering ownership, precisely scoped

My ownership covers the engineering system—model deployment, backend, frontend, and infrastructure—in collaboration with the project team. The application is actively used by 50+ legal professionals, with engineering responsibilities spanning deployment and the user-facing product.

The problem

Scientists needed a consistent way to evaluate production models and compare experimental versions without relying on the platform team for every run.

My contribution

Designed and built the platform from scratch with two complementary workflows: Steady State for continuous production evaluation, and On-Demand for development-time experiments.

The outcome

• Continuous evaluation for 10 production models.
• Self-service evaluation across 80+ model versions and experiments.
• Comparable accuracy, precision, recall, and F1 metrics without platform-team intervention.

The problem

Dedicated infrastructure for individual publishers created operational overhead and made onboarding more complex.

My contribution

Architected Shared Cell and led its implementation, decomposing the work across 5–6 engineers. Designed tenant isolation across traffic throttling, logging, observability, and data access.

The outcome

• Became the standard production deployment model for the platform.
• Reduced infrastructure and operational overhead.
• Simplified publisher onboarding while preserving tenant isolation.

The problem

Planning audio-description work required manual title selection, vendor allocation, and ordering, with evolving budget and operational constraints.

My contribution

Own development completely. Work directly with stakeholders through demos and feedback to define requirements and UX, design capacity-aware allocation, and implement frontend, backend, and service integrations. Designed budget semantics, approval flows, and server-side safeguards around vendor commitments; anticipated extensibility needs before formal requests.

Delivered so far · development continues

• Built the allocation, review, ordering, and fulfillment-tracking workflow across frontend and backend.
• Active stakeholder iteration is shaping requirements and usability as development continues.
• Built server-side capacity validation and duplicate-order safeguards to protect consequential actions.

Start with the allocation problem, not the infrastructure

Built a proof of concept and validated the allocation approach with product and selection stakeholders on July candidate data before building the AWS platform. Used series-level value ranking, preferred-vendor routing, and remaining-capacity fallback while keeping each series with one vendor. Replaced fuzzy partner matching with exact identifiers to avoid false positives.

Own the product loop as well as the code

Turn stakeholder demos into tracked feedback, write interaction decisions into the UI specification, then implement and verify them in the real browser. Designed run history, month-level ordering, budget summaries, and shareable URL state so users can understand both current decisions and past actions.

Make consequential actions explicit

Budget reflects work actually ordered, not simply allocated. Finalizing a new run supersedes the prior run, and ordering is restricted to the latest finalized selection. Added order previews, server-side capacity checks, and duplicate-placement guards; avoided blind retries that could commit vendor work twice.

Build for operational scale

Implemented streaming-friendly candidate ingest, asynchronous ordering, and event-driven fulfillment tracking with periodic reconciliation. Replaced many sequential result reads with presigned object-storage downloads and precomputed summaries. Object-storage downloads and precomputed summaries address the result-loading bottleneck without requiring sequential API reads.

The problem

High-concurrency live sports required predictable ad-serving capacity while keeping compute costs under control.

My contribution

Led performance and capacity engineering. Designed audience-targeting and deal-matching optimizations, alongside broader work to increase live-flow throughput.

The outcome

• Specific worst-case path improved from 75 to 225 TPS/host, reducing required compute capacity by approximately 67%.
• Broader performance work pushed live-flow capacity toward 1,000+ TPS/host.
• Helped avoid an estimated $10–15M in EC2 costs over the TNF season.

The problem

Content teams needed to identify localization gaps, prioritize them by projected viewership impact, and turn approved opportunities into generation orders.

My contribution

Helping build the broader platform. Built the end-to-end Asset Recommendations pipeline used by major global studios and content partners; re-architected processing of approximately 1.4 GB model outputs and added data-quality validation. Built secure integrations for automated asset-generation orders.

The outcome

• 99.91% production data-quality validation pass rate.
• Recommendations prioritized by projected viewership impact.
• Approved opportunities connected to downstream AI generation and quality-validation workflows.

The problem

Complex UI-generation prompts exceeded synchronous request time limits, preventing users from receiving completed results.

My contribution

Migrated the backend from Lambda and API Gateway to Amazon Bedrock AgentCore Runtime and replaced synchronous model calls with streaming. Built a standalone three-panel prototype for the generation workflow.

The outcome

• Complex prompts completed in 67 seconds after previously timing out at 300+ seconds.
• Streaming responses replaced the synchronous request path.
• A deployed backend and prototype interface for natural-language UI generation.

Change the delivery path, not just the timeout

Moved from synchronous model calls to streaming responses on Amazon Bedrock AgentCore Runtime. Built a three-panel frontend prototype alongside the backend migration. The 67-second completion result concerns complex generation requests that had previously timed out.

The problem

The ad-serving fleet needed better price-performance without compromising service latency.

My contribution

Designed the x86-to-Graviton migration and used load testing and profiling to validate performance, cost, and latency characteristics.

The outcome

• Doubled per-host throughput from 250 to 500 TPS.
• Reduced cost per request by approximately 75%.
• Projected approximately $1.4M in annual EC2 savings without degrading latency.

The problem

AI-assisted implementation increases the need for independent validation: a fast delivery loop is only useful when risky or incorrect changes can be stopped.

My contribution

Designed a four-layer delivery-safety model and implemented the first two layers: pre-review validation by an independent model-vendor agent, and repository-specific code-review rules. Added package-specific testing strategies and lint-delta checks. Broader integration-test and deployment safeguards remain future work.

Implemented checks · experiment in progress

• Built blocking validator-agent checks and repository-specific review rules.
• Validation caught real language-isolation defects before code review.
• Currently evaluating the workflow on my own changes before broader team adoption.

Independence before greater autonomy

Use a validator agent from a different model vendor to inspect a proposed change independently and block advancement when checks fail. Testing strategies differ for backend services, infrastructure, frontend, and workflow code rather than applying one generic test checklist.

Turn failure modes into explicit rules

Added checks for newly introduced lint errors and repository-specific invariants such as keeping language-scoped reads isolated. Validation caught actual isolation defects before review; the useful signal is defects blocked, not the volume of generated code.

The boundary of the experiment

The first two layers—pre-review validation and code-review rules—are implemented. The longer-term design adds pre-production integration tests, alarm gates, bake time, canaries, and rollback. The next stage is to evaluate those safeguards before expanding autonomy and adoption.

The problem

Facility managers needed a clearer view of package movement and deadline risk across fulfillment workflows so they could decide where to intervene.

My contribution

Built the facility-operations visibility platform, translating operational needs into tooling that connected package state, movement, risk, and manager action.

The outcome

• Surfaced shipments at risk of missing operational deadlines.
• Made package state and movement easier for facility managers to track.
• Helped managers prioritize interventions across fulfillment workflows.

The problem

Updated RBI requirements changed how recurring payments and payment data had to be handled, creating a business-continuity requirement for Prime Video in India.

My contribution

Built e-mandate support for Prime and Prime Video Channels subscriptions and payment-data migration and localization workflows for Indian customer data.

The outcome

• Enabled recurring Prime and Channels payments under updated RBI requirements.
• Supported payment-data residency compliance in India.
• Helped Prime Video continue operating subscription services through the regulatory transition.